conclude that learning effects are more important than selection effects for explaining differences between inexperienced and …

the context of daily life does not differ between the two groups. We therefore conclude that learning through experience …

conclude that learning effects are more important than selection effects for explaining differences between inexperienced and …

opportunities to identify learning rules, since on networks (compared to, e.g., random matching) more rules differ in terms of their … information they consult before making their choices. We use these data to estimate learning types using finite mixture models … find that learning depends on network position. Participants in more complex environments (with more network neighbors …

tariff rates as well as ownership proportions. Additionally, we examine convergence and learning in this setting. While …

opportunities to identify learning rules, since on networks (compared to, e.g., random matching) more rules differ in terms of their … information they consult before making their choices. We use these data to estimate learning types using finite mixture models … find that learning depends on network position. Participants in more complex environments (with more network neighbors …

games offer new opportunities to identify learning rules, since on networks (compared to e.g. random matching) more rules … participants choose and which information they consult before making their choices. We use this information to estimate learning … types using maximum likelihood methods. There is substantial heterogeneity in learning types. However, the vast majority of …